Research ArticleDEVELOPMENTAL NEUROSCIENCE

Sleepmore in Seattle: Later school start times are associated with more sleep and better performance in high school students

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Science Advances  12 Dec 2018:
Vol. 4, no. 12, eaau6200
DOI: 10.1126/sciadv.aau6200
  • Fig. 1 Delayed school start times result in later sleep offset and longer sleep.

    Mean student activity waveforms and sleep summaries between years for school (A and B) and nonschool days (C and D). For both (A) and (C), there was a significant effect of time, year, and the interaction (see text). **P < 0.01, difference between years (Sidak’s comparisons). For (B), there is a significant delay in sleep offset (P = 0.0007), but not sleep onset (P = 0.0459), on weekdays in 2017 as compared to 2016, resulting in a significant increase of sleep duration on school days in 2017 (P = 0.0007); P < 0.017 threshold for significance for Wilcoxon signed-rank test corrected for multiple comparisons. The same analysis of sleep parameters on nonschool days shows no difference between years (D) [n = 84 (2017, school day) and n = 94 (2016, school day); n = 76 (2017, nonschool day) and n = 81 (2016, nonschool day)]. For (B) and (D), values represent median, and bars represent interquartile range. Sleep offset was also tested through generalized linear models (see text). Each student contributed at least five nights for the school-day data and three nights for the nonschool data. NS, not significant.

  • Fig. 2 Delayed school start times result in later exposure to light in the morning but not in the evening.

    (A) Mean student light exposure waveforms between years for school and nonschool days. During school days, students appear to have a delay in morning light exposure but not in evening light exposure. This delay is not evident in the data from nonschool days. (B) For both years, exposure to light is delayed in weekends relative to weekdays. (C) Because of the non-normal nature of the light data, the times for first and last exposure to 50-lux light on school, and nonschool days were tested for each year using a two-way ANOVA. There was a significant effect of day of week (school or nonschool) and year but not of the interaction (see Table 1); ****P < 0.0001, significant difference between years (Sidak’s multiple comparisons). No difference was observed on nonschool days nor in the timing of the last daily exposure for school or nonschool days.

  • Fig. 3 Delayed school start times are associated with higher grades, reduced sleepiness, and improved attendance and punctuality.

    (A and B) Box plots of student performance and daytime sleepiness. Generalized linear models indicated that student performance, as measured by second-semester grades, was significantly higher (*P = 0.0261), whereas daytime sleepiness was significantly lower (*P = 0.0370) in 2017 than 2016. First-period absence (C) and tardy (D) data were compared between years using a χ2 test. Students from FHS but not from RHS had a significant reduction in absences and tardies (*P < 0.0001) in 2017 as compared to 2016. Numbers within boxes in (A) and (B) represent medians, and numbers in bars in (C) and (D) represent absolute value.

  • Table 1 Two-way ANOVA results for first and last time of daily exposure to 50-lux light intensity.
    YearDay of the weekInteraction
    First daily 50-lux light exposureF(1, 331) = 18.2P < 0.0001F(1, 331) = 258.1P < 0.0001F(1, 331) = 3.7P = 0.0557
    Last daily 50-lux light exposureF(1, 331) = 6.2P = 0.0136F(1, 331) = 111.0P < 0.0001F(1, 331) = 0.01P = 0.92

Supplementary Materials

  • Supplementary material for this article is available at http://advances.sciencemag.org/cgi/content/full/4/12/eaau6200/DC1

    Table S1. Demographics of students in each of the high schools included in the study.

    Fig. S1. Probability of a light measurement (among all individuals recorded) being below a threshold (X in legend) throughout the day.

    Fig. S2. Probability distribution of light measurements across all watch data from Seattle high school students in 2016 and 2017.

    Fig. S3. Representative actogram of a student in which the Actiwatch algorithm for sleep offset detection missed a sleep offset (white arrow).

  • Supplementary Materials

    This PDF file includes:

    • Table S1. Demographics of students in each of the high schools included in the study.
    • Fig. S1. Probability of a light measurement (among all individuals recorded) being below a threshold (X in legend) throughout the day.
    • Fig. S2. Probability distribution of light measurements across all watch data from Seattle high school students in 2016 and 2017.
    • Fig. S3. Representative actogram of a student in which the Actiwatch algorithm for sleep offset detection missed a sleep offset (white arrow).

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